Identification of the Most Critical Factors in Bankruptcy Prediction and Credit Classification of Companies

Document Type : Research Paper

Authors

1 Professor, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran

2 Assistant Professor, Department of Finance and Accounting, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran

3 PhD in Financial Management, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran

4 Assistant Professor, Department of Industrial Management, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran

5 Professor, Department of Financial Management, Faculty of Management, University of Tehran, Tehran, Iran

Abstract

Banks and financial institutions strive to develop and improve their credit risk evaluation methods to reduce financial loss resulting from borrowers’ financial default. Although in previous studies, many variables obtained from financial statements – such as financial ratios – have been used as the input to the bankruptcy prediction process, seldom a machine learning method based on computing intelligence has been applied to select the most critical of them. In this research, the data from companies that are were listed in Tehran’s Stock Exchange and OTC market during 26 years since 1992 to 2017 has been investigated, with 218 companies selected as the study sample. The ant colony optimization algorithm with k-nearest neighbor has been used to feature the selection and classification of the companies. In this study, the problem of the imbalanced dataset has been solved with the under-sampling technique. The results have shown that variables such as EBIT to total sales, equity ratio, current ratio, cash ratio, and debt ratio are the most effective factors in predicting the health status of companies. The accuracy of final research model is estimated that the bankruptcy prediction ranges between 75.5% to 78.7% for the training and testing sample.

Keywords

Main Subjects


Article Title [فارسی]

شناسایی مهمترین عوامل در پیش‌بینی ورشکستگی و طبقه‌بندی اعتباری شرکت‌ها

Authors [فارسی]

  • غلامرضا جندقی 1
  • علیرضا سارنج 2
  • رضا رجایی 3
  • احمدرضا قاسمی 4
  • رضا تهرانی 5
1 استاد، دانشکده مدیریت و حسابداری، پردیس فارابی، دانشگاه تهران، قم، ایران
2 استادیار، گروه مدیریت مالی و حسابداری، دانشکده مدیریت و حسابداری، پردیس فارابی، دانشگاه تهران، قم، ایران
3 دکترای مدیریت مالی، دانشکده مدیریت و حسابداری، پردیس فارابی، دانشگاه تهران، قم، ایران
4 استادیار، گروه مدیریت مالی و حسابداری، دانشکده مدیریت و حسابداری، پردیس فارابی، دانشگاه تهران، قم، ایران
5 استاد، گروه مدیریت مالی، دانشکده مدیریت، دانشگاه تهران، تهران، ایران
Abstract [فارسی]

بانک ها و موسسات مالی کوشش می کنند که روش­های ارزیابی ریسک اعتباری­یشان را به منظور کاهش زیان مالی ناشی از نکول مالی قرض­گیرندگان، توسعه داده و بهبود بخشند. هر چند در مطالعات گذشته، تعداد زیادی از متغیرهای مستخرج از صورت­های مالی شامل نسبت­های مالی به عنوان ورودی فرایند پیش­بینی ورشکستگی مورد استفاده قرار گرفته شده بود، کمتر یک روش یادگیری ماشینی که بر اساس هوش محاسباتی باشد، در انتخاب کلیدی­ترین متغیرها به کار گرفته شده بود. در پژوهش حاضر، داده­های شرکت­های حاضر در بازار بورس تهران و فرابورس در طول 26 سال از 1992 تا 2017 به عنوان جامعه پژوهش مورد بررسی قرار گرفت و 218 شرکت به عنوان نمونه انتخاب شد و الگویتم کلونی مورچگان به همراه الگوریتم نزیکترین k همسایگی به منظور انتخاب ویژگی و طبقه­بندی شرکت­ها مورد استفاده قرار گرفت. در این پژوهش مساله نامتقارن بودن مجموعه داده­ها با تکنیک زیرنمونه­برداری حل شده است. نتایج نشان می­دهد متغیرهایی  از قبیل نسبت EBIT به فروش کل، مالکانه، جاری، وجه نقد و بدهی، موثرترین عوامل در پیش­بینی وضعیت سلامت اعتباری شرکت­ها هستند. مدل نهایی پژوهش قادر به تخمین احتمال ورشکستگی با دقت بین 75.5 تا 78.7 درصد برای نمونه آموزش و آزمون می­باشد.

Keywords [فارسی]

  • ریسک اعتباری
  • احتمال نکول
  • پیش‌بینی ورشکستگی
  • نزدیک‌ترین k همسایگی
  • الگوریتم کلونی مورچگان
  • داده‌های نامتقارن
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